To run PEFT update conf/config.yaml:
defaults:
- peft: baichuan2/squad
stages:
- peft
Execute launcher pipeline: python3 main.py
Configuration
Default configurations for PEFT with squad can be found in conf/peft/baichuan2/squad.yaml.
Fine-tuning configuration is divided into four sections run, trainer, exp_manger and model.
run:
name: peft_baichuan2_7b
time_limit: "04:00:00"
dependency: "singleton"
convert_name: convert_nemo
model_train_name: baichuan2_7b
convert_dir: ${base_results_dir}/${peft.run.model_train_name}/${peft.run.convert_name}
task_name: "squad"
results_dir: ${base_results_dir}/${.model_train_name}/peft_${.task_name}
Set the number of nodes and devices for fine-tuning:
trainer:
num_nodes: 1
devices: 8
model:
restore_from_path: ${peft.run.convert_dir}/results/megatron_baichuan2.nemo
restore_from_path sets the path to the .nemo checkpoint to run fine-tuning.
Set tensor parallel and pipelien parallel size for different model sizes.
For 7B PEFT:
model:
tensor_model_parallel_size: 1
pipeline_model_parallel_size: 1
For 13B PEFT:
model:
tensor_model_parallel_size: 2
pipeline_model_parallel_size: 1
Set PEFT specific configruation:
model:
peft:
peft_scheme: "lora"
peft_scheme sets the fine-tuning scheme to be used. Supported schemes include: lora, adapter, ia3, ptuning.